Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Research Postgraduate



f.laumann18 Website




603Weeks BuildingSouth Kensington Campus





I am a PhD student at the Department of Mathematics, investigating the causal interlinkages amongst the United Nations' Sustainable Development Goals (SDGs). I view the 17 SDGs with their 169 targets as a network of 17 or 169 nodes, respectively, and try to find directed causal edges between them. Data for the indicators of the SDGs are publicly available here and I would like to see many more data scientists working with it.

Some of the results of my research can be seen here.

My research interests lie in kernel methods, causal discovery and network theory, and I find it interesting to use them to learn more about the connections between macro-economics, societies, and natural environments — which eventually underpin the urge to govern sustainably.

I am supervised by Mauricio Barahona and work closely together with Julius von Kügelgen.

I obtained an MSc in Engineering from the Technical University of Denmark (DTU) and Aarhus University with a focus on machine learning.

Besides my PhD studies, I co-founded a natural language processing (NLP) startup called NeuralSpace.


See a full list of my publications on Google Scholar.

Some selected publications:

Laumann F, von Kügelgen J, Uehara TH, Barahona M. Complex interlinkages, key objectives, and nexuses among the Sustainable Development Goals and climate change: a network analysis. The Lancet Planetary Health. 2022 May 1;6(5):e422-30.

Laumann F, Kügelgen JV, Barahona M. Kernel Two-Sample and Independence Tests for Nonstationary Random Processes. Engineering Proceedings. 2021 Jun 30;5(1):31.

Laumann F, Tambo T. Enterprise architecture for a facilitated transformation from a linear to a circular economy. Sustainability. 2018 Oct 25;10(11):3882.



Laumann F, von Kuegelgen J, Barahona M, 2021, Kernel two-sample and independence tests for non-stationary random processes, ITISE 2021 (7th International conference on Time Series and Forecasting),, Pages:1-13

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